An algorithm for drug-resistant epilepsy in Danish national registers.

Autor:	Bølling-Ladegaard E; Department of Clinical Medicine, Neurology, Aarhus University, 8200 Aarhus, Denmark., Dreier JW; The National Center for Register-based Research, School of Business and Social Sciences, Aarhus University, 8200 Aarhus, Denmark., Christensen J; Department of Clinical Medicine, Neurology, Aarhus University, 8200 Aarhus, Denmark.; The National Center for Register-based Research, School of Business and Social Sciences, Aarhus University, 8200 Aarhus, Denmark.; Department of Neurology, Aarhus University Hospital, Affiliated Member of the European Reference Network EpiCARE, 8200 Aarhus, Denmark.
Jazyk:	angličtina
Zdroj:	Brain : a journal of neurology [Brain] 2024 Sep 10. Date of Electronic Publication: 2024 Sep 10.
DOI:	10.1093/brain/awae286
Abstrakt:	Patients with drug-resistant epilepsy (DRE) have increased risks of premature death, injuries, psychosocial dysfunction, and a reduced quality of life. Identification of persons with DRE in administrative data can allow for effective large-scale research, and we therefore aimed to construct an algorithm for identification of DRE in Danish nation-wide health registers. We used a previously generated sample of 525 persons with medical record-validated incident epilepsy between 2010-2019, of which 80 (15%) fulfilled International League Against Epilepsy (ILAE) criteria of DRE at the time of the latest contact - this cohort was considered the gold standard. We linked information in the validated cohort to Danish national health registers and constructed register-based algorithms for identification of DRE-cases. The accuracy of each algorithm was validated against the medical record-validated gold standard. We applied the best performing algorithm according to test accuracy (F1 score) to a large cohort with incident epilepsy identified in the Danish National Patient Registry between 1995 and 2013 and performed descriptive and logistic regression analyses to characterize the cohort with DRE as identified by the algorithm. The best performing algorithm in terms of F1 score was defined as 'fillings of prescriptions for ≥ 3 distinct antiseizure medications (ASMs) within 3 years or acute hospital visit with epilepsy/convulsions following fillings of prescriptions for two distinct ASMs' (sensitivity 0.59, specificity 0.93, positive predictive value 0.59, negative predictive value 0.92, area under the receiver operating characteristic curve 0.77, and F1 score 0.595). Applying the algorithm to a register-based cohort of 83,682 individuals with incident epilepsy yielded 8,650 cases (10.3 %) with DRE. In multivariable logistic regression analysis, early onset of epilepsy, focal or generalized epilepsy, somatic co-morbidity, and substance abuse, were independently associated with risk of being classified with DRE. We developed an algorithm for the identification of DRE in Danish national registers, which can be applied for a variety of research questions. We identified early onset of epilepsy, focal or generalized epilepsy, somatic co-morbidity, and substance abuse as risk factors for DRE. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)
Databáze:	MEDLINE
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